File size: 1,875 Bytes
1073af4 53602c4 1073af4 34d9e47 53602c4 1073af4 6d8ba66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
# Load the model
model = load_model('model.h5', compile=False)
# Image preprocessing function
def process_image(img):
# Resize, normalize, and add batch dimension
img = img.resize((128, 128))
img = np.array(img)
img = img / 255.0 # Normalize pixel values
img = np.expand_dims(img, axis=0) # Add batch dimension (1, 128, 128, 3)
return img
# App title
st.title('Age Prediction from Image')
st.write("Upload a photo to predict the subject's age.")
# Sidebar instructions
st.sidebar.header("Instructions")
st.sidebar.write("""
1. Upload a clear face photo
2. Model will analyze facial features
3. Predicted age appears below image
4. This dataset contains images of people generated by Stable Diffusion.
For each image, you are to determine how old the person is.
Ages will range between 20 and 90 years old.
""")
# File uploader widget
file = st.file_uploader('Choose an image (JPG, JPEG, PNG)', type=['jpg', 'jpeg', 'png'])
if file is not None:
# Convert to RGB if image has alpha channel (critical fix)
img = Image.open(file).convert('RGB')
# Display uploaded image
st.image(img, caption='Uploaded Image', use_column_width=True)
# Process and predict
processed_image = process_image(img)
prediction = model.predict(processed_image)
prediction = np.round(prediction).astype(int) # Round to nearest integer
# Show prediction
st.subheader("Prediction Result:")
st.markdown(f"**Estimated Age:** {prediction[0][0]} years")
# Disclaimer
st.info("""
Note: Predictions are based on patterns learned during training.
Results may vary with image quality and lighting conditions.
""")
st.divider()
else:
st.write("Please upload an image to begin.") |